Concept

Deviance information criterion

Related lectures (12)

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores model choice, prediction, and forecasting techniques in time series analysis.

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Presents the proof of the Central Limit Theorem using Lindeberg's principle.

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.